Development and validation of a brachycephalic risk (BRisk) score to predict the risk of complications in dogs presenting for surgical treatment of brachycephalic obstructive airway syndrome
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a preoperative brachycephalic risk (BRisk) score that objectively and accurately predicts the risk of major complications or death in dogs undergoing corrective surgery for brachycephalic obstructive airway syndrome (BOAS). STUDY DESIGN: Retrospective multicenter cohort study. SAMPLE POPULATION: Score development n = 233 dogs, validation n = 50 dogs. METHODS: Data were collected on signalment, medical history, reason for presentation, physical examination, and preoperative diagnostic findings. The primary outcome measures included risk of major complications (requirement for postoperative oxygen support for >48 hours or postoperative temporary/permanent tracheostomy) or death within the hospitalization period. The score was developed by using data from two centers and was validated in a third center. The 10-point BRisk score was modeled on breed, history of previous surgery, concurrent procedures, body condition score, airway status, and admission rectal temperature. RESULTS: The score was associated with negative outcome (P < .0001) and discriminated well in both the construction (area under the receiver operator characteristic [AUROC] = 0.83) and validation groups (AUROC = 0.84). Dogs with scores >3 were 9.1 times more likely to have a negative outcome (95% CI = 3.9-21.2) compared with dogs with scores ≤3. CONCLUSION: The BRisk score developed from admission data in this study accurately rated the risk of negative outcome of dogs undergoing corrective surgery for BOAS. CLINICAL SIGNIFICANCE: Preoperative determination of the BRisk score may assist triage, management of owner expectations, decision making regarding intervention selection, and characterization of populations in clinical research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".